Projects are by definition a risky thing. Risk means that it might take longer, they do not meet their goals or will be more expensive than expected. This blog post will describe a simple but effective way of managing and reducing risks.

Especially projects in the IT world are likely to fail. In the last 20 years around 20%-30% of projects failed without delivering any value. Further 30% were challenged meaning they overrun budget or time.

In the IT world we have a lot of systems to organize our daily business. Process models tell us how to do our work but what about models for outside IT? How should for example an accountant office organize their workforce and their tasks?
This blog post will introduce a queue based model that emphasizes on process throughput and process enhancements for knowledge workers. It is inspired by Kanban.

According to the CHAOS-Report around 70% of IT projects are challenged or completely failing. In the last 10 years these number did not change a lot. It is still the case that around 70% of IT projects fail. This blog post will show you some techniques to control your project and make sure that it is completed in time and in budget.

There are a lot of problems in the real world about perceiving the correct chance. Studies show that we humans are biased by fast decisions and that we are sometimes not exploring the problem domain long enough to take the best decision. This blog post will show you the optimal decision to pay the best price for an auction on ebay and to find the best gas station if you are driving a known route.

In this blog post we will consider that you are already running around 10,000 campaigns across 5 different channels and you know which of these are running well and producing a lot of profit and which of these are running poor. At every beginning of the month you have to assign 100,000 € to the best running campaigns to get the most revenue.

In this blog post we will show an example of the methods that the incentergy platform applies to do mathematically proven optimal decisions for online marketing budget allocations. The system must make some assumptions about how the revenue which was generated by the marketing channel. Under these assumptions the shown behavior is optimal. This means that there is no way to spend less money to win this explore-exploit-dilemma.